Automated Support Quality Audits

100% Quality Assurance & Agent Development Universal Rubric-Based Scoring AI scores every single agent interaction across a customizable rubric: Greeting & Tone (did they acknowledge the issue empathetically?), Technical Accuracy (was the solution correct?), Completeness (did they address all parts of the question?), Proactiveness (did they anticipate follow-up needs?), and Policy Compliance (did they follow escalation procedures correctly?). Each dimension is scored 1-5. Calibration & Consistency AI scoring is calibrated against human QA reviews

monthly. A sample of AI-scored tickets is independently scored by QA leads, and discrepancies are used to refine the model. This ensures AI and human QA are aligned — and identifies where human reviewers are inconsistent with each other. Pattern Recognition Beyond individual ticket scoring, AI identifies agent performance patterns: Which issue types does Agent X struggle with? (training opportunity), Which time-of-day do scores drop? (fatigue/capacity issue), Which customer segments receive lower-quality service? (bias detection). Personalized Coaching

Plans AI generates a weekly 'Development Card' for each agent: Strengths to repeat ('Your empathy scores are top-quartile'), Areas to improve ('Technical accuracy dropped 12% this week — here are 3 example tickets with suggested better responses'), and Micro-Learning assignment ('Complete the 10-minute module on API troubleshooting'). Team Performance Dashboard QA leads see aggregate team metrics: average QA score by dimension, score distribution (identifies inconsistency), improvement trends over time, and benchmark against company-wide averages.

Enables data-driven 1:1 coaching conversations instead of subjective feedback. Share AI QA scores transparently with agents — they should see their scores in real-time, not wait for a monthly review. Self-awareness drives improvement. Use 'Positive First' coaching — AI should highlight what the agent did well before areas for improvement. A 4/5 score should feel like recognition, not criticism. Celebrate improvement trajectories — an agent who improved from 3.0 to 3.8 in a month deserves more recognition than an agent who's been at 4.5 for a year. Don't

use QA scores punitively — agents who fear scoring will focus on safe, scripted responses instead of genuine customer care. QA should drive growth, not fear. Don't weight all rubric dimensions equally — Resolution Accuracy should typically be weighted higher than Greeting Quality. Customize weights to your quality philosophy. Don't ignore the limitations — AI can assess tone and accuracy but cannot evaluate judgment calls, creative problem-solving, or the nuance of knowing when to break a rule for a customer. Keep human QA for complex escalations. The

'Peer Learning' Network AI can identify 'exemplar tickets' — interactions where an agent handled a difficult situation brilliantly (high QA score + high CSAT + complex issue). Anonymize these and share them as learning materials for the team. Peer learning from real examples is 3x more effective than theoretical training. Build a living library of 'How We Do It Best' that grows automatically from your daily operations.